Image Renaissance Using Discrete Optimization

Allène, Cédric; Paragios, Nikos

doi:10.1109/icpr.2006.686

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…The proposed method is also similar to other patchbased image generation techniques such as texture transfer 30,31) , texture synthesis 30,32) , and inpainting 29,[33][34][35] . Although the aims of these techniques are different, the common point is a constraint that similar patterns of local regions overlapping each other should be found in the reference one.…”

Section: )mentioning

confidence: 90%

[Paper] Visible-Light Image Synthesis from Infrared Images Using Texture Transfer

Wakabayashi

Ikeda

Sato

2016

MTA

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We propose a novel method for synthesizing a visible-light image from a near-infrared one taken from the same viewpoint. Generating a multi-channel color image from a single-channel one is generally an ill-posed problem. As texture and color cues, the method refers to visible-light color images of the same scene but taken from a different point. The proposed method focuses on the estimation of the luminance component rather than the chrominance one because chrominance is not sensitive for human vision, and relatively easier to be estimated than luminance. Our cost function to optimize the luminance component is designed with the assumption that the synthesized image should be globally similar to the input infrared image structure and locally similar to the reference image patterns. Our experimental results show that the proposed method produces an artificial visible-light image, whose color appearance is more natural than color conversion methods, and geometrically more accurate than texture transfer-based methods.

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Section: )mentioning

confidence: 90%

[Paper] Visible-Light Image Synthesis from Infrared Images Using Texture Transfer

Wakabayashi

Ikeda

Sato

2016

MTA

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“…Exemplar-based image inpainting [9], [10], [1], [11], techniques are another group of approaches which synthesize the missing region from patches obtained from the uncorrupted regions of the image or a database. Basically, they search for optimum patches from the available ones to fill in the missing region such that the boundaries between neighboring patches in the inpainted image are smooth.…”

Section: Introductionmentioning

confidence: 99%

Image inpainting with a learned guidance vector field

Loke

Ranganath

2009

2009 7th International Conference on Information, Communications and Signal Processing (ICICS)

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Image inpainting is one of the challenging problems in image restoration. To recover the missing region, we can only rely on the information in the uncorrupted region of the input image and some prior knowledge. The latter can be learned from suitable training data or implemented through some smoothness constraints. In this paper, a new approach for image inpainting is proposed. Here, we iteratively learn a guidance vector field from training data and recover the missing region by solving the Poisson equation using the learned guidance vector field with Dirichlet boundary conditions. In addition, we also propose a method to select the best training set by using the correlation between neighboring patches of the damaged input image and training images. The experimental results on face images show that the new approach yields smooth and visually pleasing results.

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“…As the feature space, Fourier space, wavelet domain and eigenspace have been used [12,13,14]. Most of the other exemplar-based methods simply employ SSD (Sum of Squared Differences)-based pattern similarity measures [15,16,17,18,19,20,21,22,23,24]. Efros et al [15] have proposed a method that successively copies the most similar pattern from the data region to the missing region.…”

Section: Introductionmentioning

confidence: 99%

“…Although the duplication of similar textures preserves the local texture continuity in these methods, discontinuous textures are easily synthesized in the completed image. To avoid the ordering problem, recent inpainting methods employ the iterative global optimization approach [19,20,21]. In these methods, the objective functions that evaluate the pattern similarity are defined and optimized by using EM algorithm, Belief Propagation approach and graph cut approach.…”

Section: Introductionmentioning

confidence: 99%

Image Inpainting Considering Brightness Change and Spatial Locality of Textures and Its Evaluation

Kawai

Sato

Yokoya

2009

Advances in Image and Video Technology

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Image inpainting techniques have been widely investigated to remove undesired objects in an image. Conventionally, missing parts in an image are completed by optimizing the objective function using pattern similarity. However, unnatural textures are easily generated due to two factors: (1) available samples in the image are quite limited, and (2) pattern similarity is one of the required conditions but is not sufficient for reproducing natural textures. In this paper, we propose a new energy function based on the pattern similarity considering brightness changes of sample textures (for (1)) and introducing spatial locality as an additional constraint (for (2)). The effectiveness of the proposed method is successfully demonstrated by qualitative and quantitative evaluation. Furthermore, the evaluation methods used in much inpainting research are discussed.

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Image Renaissance Using Discrete Optimization

Cited by 8 publications

References 19 publications

[Paper] Visible-Light Image Synthesis from Infrared Images Using Texture Transfer

[Paper] Visible-Light Image Synthesis from Infrared Images Using Texture Transfer

Image inpainting with a learned guidance vector field

Image Inpainting Considering Brightness Change and Spatial Locality of Textures and Its Evaluation

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